A/B Testing & Multivariate Testing: From Hypothesis to Higher Conversions

Understanding A/B Testing (Split Testing)

A/B testing is a controlled experiment used to compare two versions of a webpage to determine which one performs better. Traffic is split evenly between the original version (the 'Control') and a modified version (the 'Variation'). By measuring which version leads to a higher conversion rate, you can make data-driven decisions instead of relying on guesswork. It's the most reliable way to prove that a proposed change will have a positive impact.

• One Variable at a Time: A true A/B test changes only one significant variable at a time (e.g., the headline, the CTA button color, or the hero image).
• Clear Goal: Every test must have a single, clearly defined goal metric, such as 'clicks on the primary CTA' or 'form submissions.'
• Data-Driven: It's a scientific method for website optimization, removing subjective opinions from the decision-making process.

Leveraging Multivariate Testing (MVT)

Multivariate testing takes experimentation a step further. Instead of testing one change, MVT allows you to test multiple changes on a single page simultaneously to see which combination of elements performs best. For example, on a product page, you could test three different headlines and two different hero images at the same time, creating six possible combinations.

When to Use MVT:

• High-Traffic Pages: MVT requires a large amount of traffic to produce statistically significant results for all the different combinations.
• Understanding Interactions: It's ideal for understanding how different page elements interact with each other. Does a bold headline work better with a product-focused image or a lifestyle image?
• Incremental Optimization: MVT is great for making several small refinements to a high-performing page.

Building a Strong, Data-Backed Hypothesis

A test is only as good as the hypothesis behind it. A strong hypothesis is not a random guess; it's a clear, testable statement based on insights gathered from your analytics and user research. It should articulate what you are changing, what you expect the outcome to be, and why you expect it.

A Strong Hypothesis Formula:

• Based on Observation: 'Because we saw in session recordings that users are ignoring our current text-link CTA...'
• The Change: '...we believe that changing it to a high-contrast button...'
• The Expected Outcome (with metric): '...will increase clicks on the CTA by at least 15%.'

Ensuring Statistical Significance

Statistical significance is a crucial concept in A/B testing. It's a measure of confidence that the results of your test are not due to random chance. It's essential to run your test long enough to collect a sufficient sample size and reach a high level of confidence (typically 95% or higher) before making a decision. Ending a test too early based on initial trends is a common mistake that can lead to implementing a false winner.

• Use a Sample Size Calculator: Before starting a test, determine how many visitors you need per variation to get a reliable result.
• Run for Full Business Cycles: Let your test run for at least one to two full weeks to account for variations in traffic behavior on different days of the week.
• Don't 'Peek': Avoid the temptation to stop a test the moment one variation appears to be winning. Wait for the predetermined sample size or duration to be met.